TestMAPlot

library(Glimma)
library(limma)
library(GlimmaV2)

data(lymphomaRNAseq)
rnaseq <- lymphomaRNAseq

# 7 samples
# each instance is a gene
# attribute is number of counts per sample
# data.frame(rnaseq$counts)

# add lane
groups <- data.frame(genotype=rnaseq$samples$group,
                     lane= as.character(c(rep(4,5),3,3)),
                     miscCont=c(rep(4000,5),300,250),
                     miscDisc=c("blue","red",rep("green",5)))

# add libsize
groups <- rnaseq$samples$group

# fit
design <- model.matrix(~0+groups)
contrasts <- cbind(Smchd1null.vs.WT=c(-1,1))

# convert raw counts to logCPM values by automatically extracting libsizes and normalisation factors from x
vm <- voomWithQualityWeights(rnaseq, design=design)
fit <- lmFit(vm, design=design)
fit <- contrasts.fit(fit, contrasts)
fit <- eBayes(fit)
dt <- decideTests(fit)
summary(dt)

##        Smchd1null.vs.WT
## Down               1478
## NotSig            24424
## Up                 1277

glimmaMA(fit, status=dt)

glimmaMA(fit, status=dt, status.colours=c("mediumseagreen","black","darkviolet"))